Neural noise improves brain simulation and prediction of rare events
Neural noise enables accurate internal simulation of rare events
Neural and Evolutionary Computing
Summary
The brain has to learn from events that sometimes happen only rarely, which is hard because it doesn’t get many chances to see them. This paper shows that adding some randomness, or noise, inside brain-like neural models helps these models better remember and replay rare events accurately. The researchers trained a brain-inspired network and found that without some noise, rare events were often forgotten or exaggerated. This suggests that brain noise might actually help us predict uncommon but important things more reliably.
What this means in practice
- •For neural engineers: Design brain-inspired systems that use controlled noise to better simulate and predict rare sequences of events from limited data.
- •For clinical developers: Develop diagnostic tools or therapies by modeling how altered neural noise affects brain predictions in diseases like Parkinson’s.
Authors
Heng Zhang, Pawel Herman, Zenas C. Chao
Abstract
The brain needs an accurate internal model of the world to generate predictions and guide behavior. However, it must estimate the statistical structure of the environment from limited experience. This is particularly difficult for rare events, whose observed frequencies in a limited sample may substantially under- or overestimate their true frequencies. How the brain constructs an accurate internal model despite this sampling problem remains unclear. We address this problem using a Bayesian Confidence Propagation Neural Network (BCPNN) trained on event sequences from a Markov-chain random walk with controlled event frequencies. Treating the underlying Markov structure as the ground truth, we train the network on limited sample of event sequences and then allow it to generate autonomous replay based on the learned structure. We evaluate replay fidelity at the levels of both marginal event frequencies and conditional transition structure. We find that moderate neural noise, modeled as temporally correlated random fluctuations in unit activity during replay, is critical for faithful internal simulation. Without this variability, deterministic replay systematically under- or overrepresents rare events, whereas moderate noise restores both their marginal and conditional occurrence. Moderate noise also broadens the range of parameter values that produce accurate replay, making the model more robust to parameter variation. Together, these results support noise-assisted internal simulation as a potential mechanism for compensating for sampling errors arising from limited experience. Our model also provides a testable framework for investigating how altered neural variability may impair internal-model fidelity in disorders such as Parkinson's disease.